Overview

This page describes how to go about writing Load functions and Store functions using the API available in Pig 0.7.0.

The main motivation for the changes in Pig 0.7.0 load/store api is to move closer to using Hadoop's InputFormat and OutputFormat classes. This way pig users/developers can create new LoadFunc and StoreFunc implementation based on existing Hadoop InputFormat and OutputFormat classes with minimal code. The complexity of reading the data and creating a record will now lie in the InputFormat and likewise on the writing end, the complexity of writing will lie in the OutputFormat. This enables Pig to easily read/write data in new storage formats as and when an Hadoop InputFormat and OutputFormat is available for them.

A general note applicable to both LoadFunc and StoreFunc implementations is that the implementation should use the new Hadoop 20 API based classes (InputFormat/OutputFormat and related classes) under the org.apache.hadoop.mapreduce package instead of the old org.apache.hadoop.mapred package.

How to implement a Loader

LoadFunc abstract class has the main methods for loading data and for most use cases it would suffice to extend it. There are 3 other optional interfaces which can be implemented to achieve extended functionality:

LoadMetadata has methods to deal with metadata - most implementation of loaders don't need to implement this unless they interact with some metadata system. The getSchema() method in this interface provides a way for loader implementations to communicate the schema of the data back to pig. If a loader implementation returns data comprised of fields of real types (rather than DataByteArray fields), it should provide the schema describing the data returned through the getSchema() method. The other methods are concerned with other types of metadata like partition keys and statistics. Implementations can return null return values for these methods if they are not applicable for that implementation.

LoadPushDown has methods to push operations from pig runtime into loader implementations - currently only projections .i.e the pushProjection() method is called by Pig to communicate to the loader what exact fields are required in the pig script. The loader implementation can choose to honor the request or respond that it will not honor the request and return all fields in the data. If a loader implementation is able to efficiently return only required fields, it should implement LoadPushDown to improve query performance. (Irrespective of whether the implementation can or cannot return only the required fields, if the implementation also implements getSchema(), the schema returned in getSchema() should be for the entire tuple of data.)

LoadCaster has methods to convert byte arrays to specific types. A loader implementation should implement this if casts (implicit or explicit) from DataByteArray fields to other types need to be supported.

The LoadFunc abstract class is the main class to extend for implementing a loader. The methods which need to be overriden are explained below:

getInputFormat() :This method will be called by Pig to get the InputFormat used by the loader. The methods in the InputFormat (and underlying RecordReader) will be called by pig in the same manner (and in the same context) as by Hadoop in a map-reduce java program. If the InputFormat is a hadoop packaged one, the implementation should use the new API based one under org.apache.hadoop.mapreduce. If it is a custom InputFormat, it should be implemented using the new API in org.apache.hadoop.mapreduce. If a custom loader using a text-based InputFormat or a file based InputFormat would like to read files in all subdirectories under a given input directory recursively, then it should use the PigFileInputFormat and PigTextInputFormat classes provided in org.apache.pig.backend.hadoop.executionengine.mapReduceLayer. This is to work around the current limitation in Hadoop's TextInputFormat and FileInputFormat which only read one level down from provided input directory. So for example if the input in the load statement is 'dir1' and there are subdirs 'dir2' and 'dir2/dir3' underneath dir1, using Hadoop's TextInputFormat or FileInputFormat only files under 'dir1' can be read. Using PigFileInputFormat or PigTextInputFormat (or by extending them), files in all the directories can be read.

setLocation() :This method is called by Pig to communicate the load location to the loader. The loader should use this method to communicate the same information to the underlying InputFormat. This method is called multiple times by pig - implementations should bear this in mind and should ensure there are no inconsistent side effects due to the multiple calls.

prepareToRead() : Through this method the RecordReader associated with the InputFormat provided by the LoadFunc is passed to the LoadFunc. The RecordReader can then be used by the implementation in getNext() to return a tuple representing a record of data back to pig.

getNext() :The meaning of getNext() has not changed and is called by Pig runtime to get the next tuple in the data - in this method the implementation should use the the underlying RecordReader and construct the tuple to return.

The following methods have default implementations in LoadFunc and should be overridden only if needed:

setUdfContextSignature():This method will be called by Pig both in the front end and back end to pass a unique signature to the Loader. The signature can be used to store into the UDFContext any information which the Loader needs to store between various method invocations in the front end and back end. A use case is to store RequiredFieldList passed to it in LoadPushDown.pushProjection(RequiredFieldList) for use in the back end before returning tuples in getNext(). The default implementation in LoadFunc has an empty body. This method will be called before other methods.

relativeToAbsolutePath():Pig runtime will call this method to allow the Loader to convert a relative load location to an absolute location. The default implementation provided in LoadFunc handles this for FileSystem locations. If the load source is something else, loader implementation may choose to override this.

Example Implementation

The loader implementation in the example is a loader for text data with line delimiter as '\n' and '\t' as default field delimiter (which can be overridden by passing a different field delimiter in the constructor) - this is similar to current PigStorage loader in Pig. The implementation uses an existing Hadoop supported !Inputformat - TextInputFormat as the underlying InputFormat.

How to implement a Storer

StoreFunc abstract class has the main methods for storing data and for most use cases it should suffice to extend it. There is an optional interface which can be implemented to achieve extended functionality:

StoreMetadata: This interface has methods to interact with metadata systems to store schema and store statistics. This interface is truely optional and should only be implemented if metadata needs to stored.

The methods which need to be overridden in StoreFunc are explained below:

getOutputFormat(): This method will be called by Pig to get the OutputFormat used by the storer. The methods in the OutputFormat (and underlying RecordWriter and OutputCommitter) will be called by pig in the same manner (and in the same context) as by Hadoop in a map-reduce java program. If the OutputFormat is a hadoop packaged one, the implementation should use the new API based one under org.apache.hadoop.mapreduce. If it is a custom OutputFormat, it should be implemented using the new API under org.apache.hadoop.mapreduce. The checkOutputSpecs() method of the OutputFormat will be called by pig to check the output location up-front. This method will also be called as part of the Hadoop call sequence when the job is launched. So implementations should ensure that this method can be called multiple times without inconsistent side effects.

setStoreLocation(): This method is called by Pig to communicate the store location to the storer. The storer should use this method to communicate the same information to the underlying OutputFormat. This method is called multiple times by pig - implementations should bear in mind that this method is called multiple times and should ensure there are no inconsistent side effects due to the multiple calls.

prepareToWrite(): In the new API, writing of the data is through the OutputFormat provided by the StoreFunc. In prepareToWrite() the RecordWriter associated with the OutputFormat provided by the StoreFunc is passed to the StoreFunc. The RecordWriter can then be used by the implementation in putNext() to write a tuple representing a record of data in a manner expected by the RecordWriter.

putNext(): The meaning of putNext() has not changed and is called by Pig runtime to write the next tuple of data - in the new API, this is the method wherein the implementation will use the the underlying RecordWriter to write the Tuple out.

The following methods have default implementations in StoreFunc and should be overridden only if necessary:

setStoreFunc!UDFContextSignature(): This method will be called by Pig both in the front end and back end to pass a unique signature to the Storer. The signature can be used to store into the UDFContext any information which the Storer needs to store between various method invocations in the front end and back end. The default implementation in StoreFunc has an empty body. This method will be called before other methods.

relToAbsPathForStoreLocation(): Pig runtime will call this method to allow the Storer to convert a relative store location to an absolute location. An implementation is provided in StoreFunc which handles this for FileSystem based locations.

checkSchema(): A Store function should implement this function to check that a given schema describing the data to be written is acceptable to it. The default implementation in StoreFunc has an empty body. This method will be called before any calls to setStoreLocation().

Example Implementation

The storer implementation in the example is a storer for text data with line delimiter as '\n' and '\t' as default field delimiter (which can be overridden by passing a different field delimiter in the constructor) - this is similar to current PigStorage storer in Pig. The implementation uses an existing Hadoop supported OutputFormat - TextOutputFormat as the underlying OutputFormat.